Peptide Drug AI Design and Screening Platform
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The development of peptide therapy and candidate screening constitute a complex interdisciplinary task. In recent years, the integration of artificial intelligence technology has brought unprecedented changes to the field of peptide drug development, greatly enhancing the efficiency and accuracy of the prediction and screening process. Creative Peptides is at the forefront of the wave of innovation, equipped with an industry-leading AI enabling platform focused on peptide drug design and screening, aiming to solve the core challenges of contemporary drug discovery. The pepride drug AI design and screening technology system built by us is a comprehensive platform integrating artificial intelligence algorithms, computational biology and experimental verification, aiming to accelerate the discovery process of pepride drugs, accurately predict the affinity between peprides and the major histocompatibility complex (MHC) through AI models, and screen the inhibitor peptide sequences of protein targets. At the same time, the peptide sequence with functional potential was explored.
Highlights of our peptide drug AI platform
Peptide-MHC binding prediction model
The interaction between peptides and MHC molecules plays a decisive role in immune response and is critical for vaccine design and immunotherapy. Traditional forecasting methods are often time consuming and labor intensive, but our cutting-edge AI model is a fundamental innovation. By learning from a wide range of data from large-scale databases, including examples of known peptide-MHC interactions, our model relies on deep neural networks to integrate sequence characteristics, physicochemical properties, and three-dimensional conformation information of peptides and MHCS to achieve high-precision prediction of multiple MHC alleles. The process involves rigorous data acquisition, pre-processing, feature extraction, model building and performance verification to ensure the efficiency and universality of the model.
Targeted inhibitory peptide sequence prediction model
Given the unique advantages of peptides as protein-targeted inhibitors, we used AI technology to tackle the challenge of finding efficient inhibition sequences. After identifying key targets through literature research and protein database analysis, candidate peptide sequences were generated by advanced algorithms such as GANs and RNNs, and AI models were used to evaluate the binding potential of these peptides with target proteins, and high-affinity sequences were selected. Further structural modeling and molecular dynamics simulation verified the mechanism of peptide-protein interaction, ensuring that the designed peptide not only has high specificity and potency, but also takes into account good pharmacokinetic and pharmacodynamic properties. This strategy significantly reduces the time and cost of early-stage drug development, paving a fast track for the development of groundbreaking treatments.
Functional fragment screening of peptide sequences
The selection of functional components of peptides is a key step to explore peptides with therapeutic value. Although traditional high-throughput screening techniques are effective in identifying effective peptides, they consume a lot of resources. In contrast, our AI-driven screening platform has revolutionized the process with high efficiency, predicting bioactive peptide sequences with unprecedented accuracy. This innovative process begins with building a diverse library of peptide sequences that are evaluated against a wide range of biological criteria, including binding affinity, structural stability, and in vivo availability, using cutting-edge algorithms, all of which are trained in deep learning based on a large dataset of peptides covering diverse biological activity and therapeutic scenarios.
Fragment Intelligent design: Based on the proven active regions of the peptide, we custom-designed a series of candidate peptide sequences to maximize their therapeutic potential.
AI-driven screening model: Advanced artificial intelligence models are introduced to efficiently screen functional fragments by finely matching sequence features and predicting their biological activity. This process delves into the intrinsic properties of peptides beyond the limitations of traditional methods.
Functional validation: Peptides screened by AI will undergo further in vitro empirical testing, such as cellular level analysis, to confirm their biological function and therapeutic potential, ensuring a high degree of alignment between theoretical predictions and actual effects. This series of rigorous validation steps solidifies our platform's leadership in accelerating the development of peptide therapies and lays a solid foundation for a new generation of treatment options."
Dry-wet integration methods
While AI models provide powerful predictions and insights, the ultimate validation of these predictions lies in experimental verification. At Creative Peptides, the dedicated team of computational biologists and experimental scientists work in close collaboration to ensure seamless integration between in silico predictions and in vitro/in vivo studies. This iterative feedback loop involves fine-tuning AI models based on experimental outcomes, thereby enhancing their accuracy and reliability over time. Our wet-lab capabilities include peptide synthesis, high-throughput screening assays, structural analysis, and functional characterization. By validating computational predictions through rigorous experimentation, we ensure that our peptide candidates meet the highest standards of efficacy and safety.
Training and optimization: Train and optimize AI models using experimental data (wet lab data). This includes binding affinity assays, functional assays, and other relevant biological measurements.
Iterative feedback: Incorporate experimental results back into the AI models to continuously improve their predictive performance. This iterative cycle enhances the models' accuracy and reliability over time.
Predicted peptide validation: Validate the predicted peptide sequences from the AI models through wet lab experiments. These experiments assess the biological activity and pharmacological efficacy of the peptides.
High-throughput screening: Use high-throughput screening techniques to test large numbers of peptides quickly and efficiently. Functional assays, binding assays, and other relevant tests are used to validate the AI predictions.
Data feedback: Experimental results are fed back into the AI system to refine and update the models. This continuous feedback loop ensures that the models evolve and improve based on real-world data.
Model refinement: Based on experimental validation, refine the AI models to enhance their predictive power. This process involves adjusting model parameters, incorporating new data, and possibly re-training the models with updated datasets.
Why choose Creative Peptides?
- Efficient peptide drug discovery
Large-scale peptide screening using AI models significantly shortens the development cycle. While traditional methods can take months or even years, AI models can provide results in weeks. The AI platform can automatically process large amounts of data and analysis tasks, reduce human errors, and improve work efficiency.
- High-precision prediction
Using the most advanced deep learning algorithms (such as CNN, RNN, GAN), it can be trained on large-scale data sets to provide high-precision prediction results.
According to the specific needs of customers, we customize the design of highly specific and high-affinity peptide drugs to improve the success rate.
- Comprehensive functional modules
Help develop immunotherapy drugs. Design efficient peptide inhibitors for the treatment of various diseases. Screen out peptide fragments with specific biological functions to meet different research and application needs.
- Dry and wet combination method
- Rich data resources
Utilize public databases (such as IEDB, PDB) and company internal data to ensure the diversity and comprehensiveness of model training data. Professional data preprocessing and feature extraction technology ensures data quality and consistency.
- Flexible cooperation model
Customized services: Provide personalized customized services based on customer needs, covering the entire process from target identification to peptide design and experimental verification.
Technical support: Provide professional technical support and consulting services to help customers solve technical problems and optimize the research and development process.
Training and guidance: Provide customers with platform usage training and technical guidance to help customers make full use of the advantages of the platform.
The convergence of AI and peptide drug discovery heralds a new era of biopharmaceutical innovation. At Creative Peptides, our advanced peptide drug AI design and screening technology platform leverages the strengths of both AI models and experimental validation through a dry-wet integration approach. This platform can efficiently and accurately predict and screen peptide drug sequences, providing strong support for new drug development.
FAQ
1. What is the main purpose of the peptide drug AI design and screening technology platform?
The main purpose of the platform is to accelerate the discovery and development of peptide-based drugs by integrating advanced AI models with experimental validation methods. It aims to predict peptide-MHC binding, identify peptide sequences that inhibit specific protein targets, and screen functional peptide fragments efficiently and accurately.
2. What types of peptides can this platform design and screen?
The platform can design and screen various types of peptides, including those for therapeutic purposes, such as inhibitors, agonists, and modulators targeting specific proteins, as well as peptides that bind to MHC molecules for vaccine development.
3. How does the platform predict peptide-MHC binding?
The platform uses AI models trained on large datasets of peptide-MHC binding data. It employs deep learning techniques like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to predict the binding affinity between peptides and MHC molecules based on features extracted from amino acid sequences and structural information.
4. What kind of data is required to use the platform effectively?
The platform requires high-quality experimental data for training AI models, including peptide-MHC binding affinities, protein-peptide interaction data, and functional assay results. Access to extensive databases and experimental results is essential for optimizing the AI models.
5. How are the results validated experimentally?
The results are validated using high-throughput screening techniques, functional assays, binding assays, and other relevant biological tests in the wet lab. These experiments confirm the biological activity and pharmacological efficacy of the AI-predicted peptides.
6. Can this platform be used for personalized medicine?
Yes, the platform's ability to customize peptide sequences for specific targets makes it suitable for personalized medicine applications. It can design peptides tailored to individual patient's needs, potentially improving therapeutic outcomes.
7. What are the steps to start using the platform?
- Provide relevant data for training the AI models.
- Define the specific targets and objectives of the peptide design project.
- Utilize the platform's computational tools to predict and screen peptide sequences.
- Validate the top candidates through experimental assays.
- Iterate based on feedback to refine and optimize the peptide designs.
